1. Field
The present disclosure relates generally to processing images and, in particular, to tracking objects in images. Still more particularly, the present disclosure relates to a method and apparatus for tracking closely spaced objects (CSOs) in images.
2. Background
Sensor systems may be used to detect and track different types of objects that move in an area. These different types of objects may include, for example, without limitation, aircraft, unmanned aerial vehicles (UAVs), spacecraft, satellites, missiles, automobiles, tanks, unmanned ground vehicles (UGVs), people, animals, and other suitable types of objects. Further, these objects may be detected and tracked using different types of sensor systems. These different types of sensor systems may include, for example, without limitation, visible light imaging systems, electro-optical (EO) imaging systems, infrared (IR) sensor systems, near-infrared sensor systems, ultraviolet (UV) sensor systems, radar systems, and other types of sensor systems.
As one illustrative example, a sensor system may be used to generate images of an area. These images may take the form of video, for example. These images may be used to detect and track objects of interest in the area. In some situations, two or more objects that are within close proximity in the area being observed may appear in a same region of an image. These objects may be referred to as a “cluster”. For example, when the lines of sight from a sensor system to two or more objects in the area being observed are within some selected proximity of each other, the portions of the image representing these objects may partially overlap such that the objects appear as a cluster in the image.
When the portions of an image representing these objects overlap more than some selected amount in the different images in a sequence of images, distinguishing between and tracking the different objects in the cluster in the sequence of images may be more difficult than desired. Some currently available methods for distinguishing between the objects in a cluster in a sequence of images may take more time, effort, and/or processing resources than desired. Further, these currently available methods may be unable to track movement of the objects with a desired level of accuracy.
Therefore, it would be advantageous to have a method and apparatus that takes into account at least some of the issues discussed above, as well as possibly other issues.